文章基本信息

标题：A Novel Approach for Background Subtraction using Generalized Rayleigh Distribution
作者：Pavan Kumar Tadiparthi ; Srinivas Yarramalle ; Nagesh Vadaparthi 等
期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2018
卷号：9
期号：9
DOI：10.14569/IJACSA.2018.090964
出版社：Science and Information Society (SAI)
摘要：Identification of the foreground objects in dynamic scenario video images is an exigent task, when compared to static scenes. In contrast to motionless images, video sequences offer more information concerning how items and circumstances change over time. Pixel based comparisons are carried out to categorize the foreground and the background based on frame difference methodology. In order to have more precise object identification, the threshold value is made static during both the cases, to improve the recognition accuracy, adaptive threshold values are estimated for both the methods. The current article also highlights a methodology using Generalized Rayleigh Distribution (GRD). Experimentation is conducted using benchmark video images and the derived outputs are evaluated using a quantitate approach.
关键词：Background subtraction; segmentation; generalized rayleigh distribution (GRD); quantitative evaluation; image analysis